Archive for the ‘Renewables’ Category

When you read or hear a statistic that country X is generating Y% of electricity via renewables it can sound wonderful, but the headline number can conceal or overstate useful progress. A few tips for readers new to the subject:

Energy is not electricity. So you need to know – were they quoting energy or electricity. For most developed nations, electricity accounts for something around 40% of total energy.

“Renewables” includes two components that are important to separate out:

hydroelectric – this is “tapped out” in most developed countries. If the “share of renewables” is say 30%, but hydro is 20% (i.e. 2/3 of the total renewables) then the expandable renewables are only 10%. This can help you see recent progress and extrapolate to possible future progress (different story in developing countries, but there is often a large human cost to creating hydroelectric projects)

biomass – if you stop burning coal and you burn wood chip instead this tips the reporting scales from “the work of Satan” to “green and renewable”, even though burning wood chip generates more CO2 emissions per unit of electricity generated. Not all biomass is like this, but as a rule of thumb, put the biomass entry into the “more investigation needed” pile before declaring victory

Nameplate is not actual – if you have a gas plant (designed to run all the time) the actual output will be about 90% or more of the nameplate (the maximum output under normal conditions), but if you have a wind farm the actual output across a year will be about 20% of the nameplate in Germany, 30% in Ireland and over 40% in Oklahoma. So if you read that “10GW of wind power” was added to Germany’s generating capacity you need to mentally convert that to about 2GW. Similar story for solar – there is a conversion factor.

If you mentally take account of these points when you hear an update, you will be with the 1% of journalists who could pass the literacy test on the progress of renewables. It’s an elite club.

Once again I’ll state that I’m not trying to knock renewables, I’m trying to promote “literacy”. Instead of hapless cheerleaders, think informed citizens..

So, onto recent data.

I’m using two stalwarts of energy reporting: IEA and BP.

IEA produce data to 2015 and quote useful units like electricity consumed in TWh. This is a unit of energy – a TWh is a billion kWh. You find kWh on your electricity bill.

BP produce data to 2016 – which is better – and breakdown renewables much better, but quote units of Mtoe – millions tons of oil equivalent. If you delve into energy industry reports, you often find mixed together in one report: kWh/TWh (energy), GJ (energy), GW (power), tcf (volume of gas), barrels of oil, mmBtu (energy in obscure British units)..

In the case of the BP report it’s not clear to me how to convert from Mtoe to GWh – they do provide a footnote but when I do the conversion I can’t reconcile the numbers using their footnote. No doubt one of our readers has gone down this rabbit hole and can illuminate us all (?). In the meantime, I took the BP numbers in Mtoe and looked up IEA % values for 2016 in TWh and worked out a conversion factor – multiply Mtoe by 0.0045. Then cross-checked with Fraunhofer ISE for Germany. This allows us to see the BP 2016 renewables breakdown in real electricity units rather than in mythical barrels of oil.

Another note – I’m not trying to generate exact figures. Every source has different values. Reconciling them is a big undertaking and very uninteresting work. I’m simply trying to get some perspective on actual renewables progress.

I don’t quote nuclear energy statistics in this article. It’s very low carbon emission, but not exactly “renewable”. The real reason for not including the numbers is that most developed countries are not significantly expanding their nuclear generation, and in Germany’s case are shutting it down. China is a different story, with a big nuclear expansion ongoing.

Germany v US

You would think that Germany, one of the leading lights in renewable energy, would be greatly outperforming the US on CO2 emissions reduction.

2005 – 2015 German CO2 reduction = 0.9% p.a

2005 – 2015 US CO2 reduction = 1.1% p.a

Over that time period the German population has stayed the same, while the US population has grown by about 9%, so we can adjust the US reduction to about 2% p.a on a per capita basis.

Now the US emissions peaked in 2005. You actually don’t need to read a report to find that out because when the US commitment to reducing CO2 emissions was announced in Paris in 2015 the commitment was a reduction “from 2005”. Being cynical about politicians never loses, and sure enough (when checking data in a report) the peak was 2005 – and the reduction from 2005 to 2015 was already about 12%.

Germany’s emission peaked in 1990 so I believe their commitment is always referenced to 1990. The story I haven’t verified is that after the collapse of the Soviet Union and the re-unification of Germany, lots of dirty heavy industry shut down and this was a big help in emissions reductions.

The US reduction looks to be – in part – due to the embrace of natural gas due to its recent very low cost (gas produces about half the CO2 of coal for the same electricity production). This is a result of the current revolution in “unconventional gas”.

When we look at CO2 emissions per kWh in 2016 the story is also surprising:

Germany – 1.3g CO2/kWh

US – 1.2g CO2/kWh

So this tells us that the GHG efficiency of electricity generation is effectively the same in both countries, slightly better in the US.

When we look at total usage (across all electricity generation, including industry) the story is what we might expect:

Germany – 19 kWh per person per day

US – 35 kWh per person per day

This tells us that the US uses almost double the electricity per person.

Changes in Renewables

I looked up a few other countries – Denmark, the UK and Spain because they have a big push into renewables; and China to contrast a rapidly developing country. The last column in the table, Total Produced, is total electricity produced from all sources, including fossil fuels and nuclear.

From BP data

The IEA values (not shown) give lower total electricity for each country. The BP figures are electricity produced and IEA figures are electricity consumed. The solar + wind value for Germany in 2016 moves from 18% to 20% of total if I use the lower IEA total.

I also looked up electricity prices in the IEA report and while I have values for 2016, I don’t have comparable values for 2006. I couldn’t find the 2006 or 2007 version of the report. Based on a variety of websites all using different methods, quoting in different currencies and from unverified sources (so not reliable) the average consumer price in Germany has gone from about 19c/kWh to 33c/kWh from 2006-2016 (US$). The US looks almost flat, perhaps from 12 to 12.5c/kWh. UK from 14 to 21c/kWh. The IEA report didn’t give a figure for Denmark.

So Germany produces about 18% of electricity from solar + wind. Its total renewables are 30% if we include biomass, and about 21% if don’t include them. As I mentioned at the start, biomass sometimes includes burning “renewable” wood chip instead of fossil fuels. Biomass is a (big) subject for another day with numerous problems and I haven’t looked at the breakdown.

The Denmark figure for total electricity is probably quite misleading – see the huge reduction in electricity production from 46 TWh to 30 TWh over 10 years. On wikipedia someone has provided a better breakdown, showing consumption as well and the consumption has dropped by just 4% over that time. Also 2006 appears to be a big outlier in electricity production. Denmark is a country connected to neighboring grids and generating lots of wind energy. So Denmark’s 2006 real figure for wind was about 20% of total consumed (not 14%) and has gone up to 43% over 10 years. On this basis Denmark could be at 80% of electricity generation by wind in 2035.

Confusion

When looking for electricity price changes, here was a random site I came across, Economists at Large:

By June 16 this year electricity generated from solar and wind power accounted for a record 61% of total electricity generated in Germany.

The actual figure for 2016 is about 18%.

If I went looking I’m sure I could find lots of sites, including “reputable” media outlets, with wide ranges of inflated figures. It’s very easy to generate confusion – quote a peak daytime value like this “Germany’s renewable output was …%… on May 28th at 1:15pm” and wait for the recyclers of mush (this includes “reputable” media outlets) to propogate it in a new way. Or quote growth figures – as in how much has been added this year. Or quote capacity added, and rely on the fact that no one understands that 10GW of wind farm only generates about 2GW on average of output in Germany. And so on.

I realize young people may expect media outlets to “fact check” but that is not their job. Their job is to generate headlines and have their stories quoted more widely.

Also, if you pay zero for your electricity because you have solar power you might think that you are generating all of your own electricity. Most of the time you would be wrong. Various governments have guaranteed feed-in tariffs for rooftop solar at well above market price.

Basic energy literacy means understanding the difference between these items.

Conclusion

I was just trying to find the core statistics for my own understanding and was especially interested in Germany.

For Germany, we could look at the 3.5x increase in solar + wind in a decade and say “amazing”. Alternatively, we could look at going from 5% to 18% of total electricity generation in 10 years and say that to get to 80% of electricity production will take another 40-50 years at the same rate and say “disappointing”.

Remember that electricity is only about 40% of energy use in most developed countries. Therefore, if you want to decarbonize the whole economy you also have to boost your electricity supply by 2.5x and switch over heating, transport, etc to electric supply.

At the moment, there are currently issues with increasing “non-synchronous” generation beyond a certain point (see V – Grid Stability As Wind Power Penetration Increases). If you read spruiking websites you will find two common suggestions, first “people said we couldn’t get past 10% and now we’re already at 20%” and second “look at Denmark”. If you like happy stories probably skip the rest of this section..

The most helpful textbook I found on the topic was Renewable Electricity and the Grid : The Challenge of Variability written by people who are trying to do it. Long story short, integrating wind energy is very easy at the start, and up to about 20% of total supply on average it doesn’t seem to present a problem. Above 20% there are questions and uncertainties. These are electricity generation and grid experts contributing to the various chapters.

The key point is that grid stability can come from who you are connected to and how.

Denmark, while a country, is really just the size of a large city (population 6M) connected to the rest of Europe and this connection provides their grid stability. Denmark produced 43% of their electricity from wind in 2016 but this is a much lower % of the grid that it is connected to. The question is not “can one small country connected to nearby large countries produce 80% of electricity from wind?” but instead “can the interconnected grid produce 80% from wind?” The answer to the first question is of course yes. The other countries provide grid stability to Denmark. When all the surrounding countries are producing wind energy at 80% of the total inter-connected grid it will be a different story.

However, this is not some fundamental physics problem, it’s an engineering problem that I’m sure can be solved. I haven’t dug in much beyond the references in Part V (referenced above) so I don’t know what issues and costs are involved.

In a few large companies I observed the same phenomenon – over here are corporate dreams and over there is reality. Team – your job is to move reality over to where corporate dreams are.

It wasn’t worded like that. Anyway, reality won each time. Reality is pretty stubborn. Of course, refusal to accept “reality” is what has created great inventions and companies. It’s not always clear what is reality and what is today’s lack of vision vs tomorrow’s idea that just needs lots of work to make a revolution. So ideas should be challenged to find “reality”. But reality itself is hard to change.

I starting checking on Carbon Brief via my blog feed a few months back. It has some decent articles although they are more “reporting press releases or executive summaries” than any critical analysis. But at least they lack hysterical headlines and good vs evil doesn’t even appear in the subtext, which is refreshing. I’ve been too busy with other projects recently to devote any time to writing about climate science or impacts, but their article today – In-depth: How a smart flexible grid could save the UK £40bn – did inspire me to read one of the actual reports referenced. Part of the reason my interest was piqued was I’ve seen many articles where “inflexible baseload” is compared with “smart decentralized grids” and “flexible systems”. All lovely words, which must mean they are better ways to create an electricity grid. A company I used to work for created a few products with “smart” in the name. All good marketing. But what about reality? Let’s have a look.

What is fascinating reading the report is that all of the points I made in previous articles in this series show up, but dressed up in a very positive way:

We’re choosing between all these great options on the best way to save money

For those who like a short story, I’ll rewrite that summary:

We’re choosing between all these expensive options trying to understand which one (or what mix) will be the least expensive. Unfortunately we don’t know but we need to start now because we’ve already committed to this huge carbon reduction by 2050. If we make a good pick then we’ll spend the least amount of money, but if we get it wrong we will be left with lots of negative outcomes and high costs for a long time

Well, when you pay for the report you should be allowed to get the window dressing that you like. That’s a minimum.

The imponderables are that wind power is intermittent (and there’s not much solar at high latitudes) so you have some difficult choices:

I’ll just again repeat something I’ve said a few times in this series. I’m not trying to knock renewable energy or decarbonizing energy. But solving a problem requires understanding the scale of the problem and especially the hardest challenges – before you start on the main project.

As a digression, there is a lovely irony about the use of the words “flexible” for renewable energy vs “inflexible” for conventional energy. Planning conventional energy grids is pretty easy – you can be very flexible because a) you have dispatchable power, and b) you can stick the next power station right next to the new demand as and when it appears. So the current system is incredibly flexible and you don’t need to be much of a crystal ball gazer. That said, it’s just my appreciation of irony and how I can’t help enjoying the excitement other people have in taking up inspirational words for ideas they like.. anyway, it has zero bearing on the difficult questions at hand.

As the article from Carbon Brief said, there’s £40bn of savings to be had. Here is the report:

The modelling for the analysis has shown that the deployment of flexibility technologies could save the UK energy system £17-40 billion cumulative to 2050 against a counterfactual where flexibility technologies are not available

Ok, so it’s not £40bn of savings. The modeling says getting it wrong will cost £40bn more than picking better options. Or if the technologies don’t appear then it will be more expensive..

What are these “flexible grid technologies”?

Demand Management

The first one is the effectively untested idea of demand management (see XVIII – Demand Management & Levelized Cost) which allows the grid operator to shift peoples’ demand to when supply is available. (Remember that the biggest current challenge of an electricity grid is that second by second and minute by minute the grid operators have to match supply with demand – this is a big challenge but has been conquered with dispatchable power and a variety of mechanisms for the different timescales). I say untested because only small-scale trials have been done with very mixed results, and some large-scale trials are needed. They will be expensive. As the report says:

Demand side response has a key role in providing flexibility but also has the greatest uncertainty in terms of cost and uptake

However, with a big enough stick you get the result you want. The question is how palatable that is to voters and what kind of stomach politicians have for voter unrest. For example, increase the cost of electricity to £100/kWhr when little is available. Once you hear that a few friends received a £10,000 bill that they can’t get out of and are being taken to court you will be running around the house turning everything off and paying close attention to the tariff changes. When the tariff soars, you are all sitting in your house in your winter coats (perhaps with a small bootleg butane heater) with the internet off, the TV off, the lights off and singing entertaining songs about your favorite politicians.

I present this not in parody, but just to demonstrate that it is completely possible to get demand management to work. Just need a strong group of principled politicians with the courage of their convictions and no fear of voters.. (yes, that last bit was parody, if you are a politician you have to be afraid of voters, it’s the job requirement).

So the challenge isn’t “the technology”, it’s the cost of rolling out the technology and how inflexible consumers are with their demand preferences. What is the elasticity of demand? What results will you get? And the timescale matters. If you need people to delay using energy by one hour, you get one result. If you need people to delay using energy by two days, you get a completely different result. There is no data on this.

Pick a few large cities, design the experiments, implement the technology and use it to test different time horizons in different weather over a two year period and see how well it works. This is an urgent task that a few countries should have seriously started years ago. Data is needed.

Storage

Table 26 in the appendices has some storage costs, which for bulk storage “Includes a basket of technologies such as pumped hydro and compressed air energy storage” and is costed in £/kW – with a range of about £700 – 1,700/kW ($900 – 2,200/kW). This is for a 12 hour duration – typical daily cycle. These increase somewhat over the time period in question (to 2050) as you might expect.

For distributed storage “Based on a basket of lithium ion battery technologies” ranges from £900 – 1,300/kW today falling to £400 – 900/kW by 2050. This is for a 2 hour duration (and a 5-year lifetime). Meaning that the cost per unit of energy stored is £450 – 650/kWhr today falling to £200 – 450/kWhr by 2050. So they don’t have super-optimistic cost reductions for storage.

The storage calculations under various scenarios range from 10-20GW with a couple of outliers (5GW and 28GW).

My back of the envelope calculation says that if you can’t expand pumped hydro, don’t build your gas plants, and do need to rely on batteries, then for a 2-day wind hiatus and no demand management you would spend “quite a bit”. This is based on the expected energy use (below) of about 60GW = 2,880 GWhr for 48 hours. Converting to kWhr we get 2,880 x 106 and multiplying by the cost of £300/kWhr = £864bn every 5 years, or £170bn per year. UK GDP is about £2,000bn per year at the moment. This gives an idea of the cost of batteries when you want to back up power for a period of days.

Backup Plants

The backup gas plants show as around 20GW of CCGT and somewhere between 30-90GW of peaking plants added by 2050 (depending on the scenario). This makes sense. You need something less expensive than storage. It appears the constraint is the requirement to cut emissions so much that even running these plants as backup for low wind / no wind is a problem.

Expected Energy Use

The consumed electricity for 2020 is given (in the appendix) as 320-340 TWhr. Dividing by the number of hours in the year gives us the average output of 36-39 GW, which seems about right (recent figures from memory were about 30GW for the UK on average).

In 2050 the estimate is for 410-610 TWhr or an average of 47-70GW. This includes electric vehicles and heating – that is, all energy is coming from the grid – so on the surface it seems too low (current electricity usage is about 40% of total energy). Still, I’ve never tried to calculate it and they probably have some assumptions (not in this report) on improved energy efficiency.

Cost of Electricity in 2050 under These Various Scenarios

n/a

Conclusion

The key challenges for large-scale reductions in CO2 emissions haven’t changed. It is important to try and identify what future cost scenarios vs current plans will result in the most pain, but it’s clear that the important data to chart the right course is largely unknown. Luckily, report summaries can put some nice window-dressing on the problems.

As always with reports for public consumption the executive summary and the press release are best avoided. The chapters themselves and especially the appendices give some data that can be evaluated.

It’s clear that large-scale interconnectors across the country are needed to deliver power from places where high wind exists (e.g. west coast of Scotland) to demand locations (e.g. London). But it’s not clear that inter-connecting to Europe will solve many problems because most of northern and central Europe will be likewise looking for power when their wind output is low on a cold winter evening. Perhaps inter-connecting to further locations, as reviewed in XII – Windpower as Baseload and SuperGrids is an option, although this wasn’t reviewed in the paper.

It wasn’t clear to me from the report whether gas plants without storage/demand management/importing large quantities of European electricity would solve the problem except for too aggressive CO2 reduction targets. It sorted of hinted that the constraint of CO2 emissions forced the gas plants to less and less backup use, even though their available capacity was still very high in 2050. Wind turbines plus interconnectors around the country plus gas plants are simple and relatively quantifiable (current gas plants aren’t really optimized for this kind of backup but it’s not peering into a crystal ball to make an intelligent estimate).

The cost of electricity in 2050 for these scenarios wasn’t given in this report.

About 100 years ago I wrote Renewables XVII – Demand Management 1 and promised to examine the subject more in a subsequent article. As with many of my blog promises (“non-core promises”) I have failed to do anything in what could be even charitably described as a “timely manner”. I got diverted by my startup.

However, in a roundabout way I came across some articles that help illuminate the energy subject better than I could. While travelling I listened via audible.com to two great books by Timothy Taylor – America and the New Global Economy and A History of the U.S. Economy in the 20th Century. It turns out that Timothy Taylor is the editor of the Journal of Economic Perspectives (and also writes a blog – the Conversable Economist – which is great quality). This journal has recently made its articles open access back to the dawn of time and I downloaded a few years of the journal.

Digressing on my digression, in one of those two books, Taylor made an interesting comment about economists views on climate change which sparked my interest in studying the IPCC working groups 2 & 3 – impacts and mitigation. Possibly some articles to come in that arena, but no campaign promises. It’s a big subject.

The Journal of Economic Perspectives, Volume 26, Number 1, Winter 2012 contains a number of articles on energy, including Creating a Smarter U .S . Electricity Grid, Paul L Joskow. I recommend reading the whole paper – well-written and accessible. He comments on some of the papers that I had already discovered. A few comments selected:

Smart grid investment on the high voltage network has only a limited ability to increase the effective capacity of transmission networks. A large increase in transmission capacity, especially if it involves accessing generating capacity at new locations remote from load centers, requires building new physical transmission capacity. However, building major new transmission lines is extremely difficult. The U.S. transmission system was not built to facilitate large movements between interconnected control areas or over long distances; rather, it was built to balance supply and demand reliably within individual utility (or holding company) service areas. While the capacity of interconnections have expanded over time, the bulk of the price differences in Table 1 are due to the fact that there is insufficient transmission capacity to move large amounts of power from, for example, Chicago to New York City. The regulatory process that determines how high voltage transmission capacity (and smart grid investments in the transmission network) is sited and paid for in regulated transmission prices is of byzantine complexity..

The U.S. Department of Energy has supported about 70 smart grid projects involving local distribution systems on a roughly 50/50 cost sharing basis, with details available at 〈http://www.smartgrid.gov/recovery_act/tracking_deployment /distribution〉. However, a full transformation of local distribution systems will take many years and a lot of capital investment. Are the benefits likely to exceed the costs? In the only comprehensive and publicly available effort at cost–benefit analysis in this area, the Electric Power Research Institute (2011a) estimates that deployment (to about 55 percent of distribution feeders) would cost between $120–$170 billion, and claims that the benefits in terms of greater reliability of the electricity supply would be about $600 billion (both in net present value). Unfortunately, I found the benefit analyses to be speculative and impossible to reproduce given the information made available in EPRI’s report..

And on demand management programs’ impacts on peak demand:

The idea of moving from time-invariant electricity prices to “peak-load” pricing where prices are more closely tied to variations in marginal cost has been around for at least 50 years..

A large number of U.S. utilities began offering time-of-use and interruptible pricing options for large commercial and industrial customers during the 1980s, either as a pilot program or as an option. More recently, a number of states have introduced pilot programs for residential (household) consumers that install smart meters of various kinds, charge prices that vary with wholesale prices, and observe demand..

Faruqui and Sergici (2010) summarize the results of 15 earlier studies of various forms of dynamic pricing, including time-of-use pricing, peak pricing, and real-time pricing.. Faruqui (2011) summarizes the reduction in peak load from 109 dynamic pricing studies, including those that use time-of-use pricing, peak pricing, and full real-time pricing, and finds that higher peak period prices always lead to a reduction in peak demand. However, the reported price responses across these studies vary by an order of magnitude, and the factors that lead to the variability of responses have been subject to very limited analysis..

Accordingly, it seems to me that a sensible deployment strategy is to combine a long-run plan for rolling out smart-grid investments with well-designed pilots and experiments. Using randomized trials of smart grid technology and pricing, with a robust set of treatments and the “rest of the distribution grid” as the control, would allow much more confidence in estimates of demand response, meter and grid costs, reliability and power quality benefits, and other key outcomes. For example, Faruqui’s (2011b) report on the peak-period price responses for 109 pilot programs displays responses between 5 to 50 percent of peak demand. An order-of-magnitude difference in measured price responses is just not good enough to do convincing cost–benefit analyses, especially with the other issues noted above. In turn, the information that emerges from these studies could be used to make mid-course corrections in the deployment strategy. Given the large investments contemplated in smart meters and complementary investments, along with the diverse uncertainties that we now face, rushing to deploy a particular set of technologies as quickly as possible is in my view a mistake.

What I observed from reading a lot of papers back when I had promised a followup article (on demand management) was lots of fluff and a small amount of substance. As Joskow says, a wide range in potential outcomes, and not much in the way of large-scale data to draw real conclusions.

In that same linked document above you can also read other papers including: Prospects for Nuclear Power, Lucas W Davis; The Private and Public Economics of Renewable Electricity Generation, Severin Borenstein. Both of these papers are excellent.

Reading the Joskow paper in JEP I thought his name was familiar and it turns out I already had three of his papers:

This paper makes a very simple point regarding the proper methods for comparing the economic value of intermittent generating technologies (e.g. wind and solar) with the economic value of traditional dispatchable generating technologies (e.g. CCGT, coal, nuclear). I show that the prevailing approach that relies on comparisons of the “levelized cost” per MWh supplied by different generating technologies, or any other measure of total life-cycle production costs per MWh supplied, is seriously flawed..

The respected Gratton Institute in Australia hosted a discussion of energy insiders – grid operators, distributors, the regulator. It’s well worth reading for many reasons. When I was thinking about this article I remembered the discussion. Here are a few extracts:

MIKE: Andrew, one of the elements in the room here is the growth in peak demand. I can put however many air conditioners I want in my house and as long as I can pay for the electricity, I can turn them on and I don’t have to worry about that. You certainly can’t regulate for it. When are we going to allow you to regulate for peak demand? Obviously it’s not in the interests of the network operators who get a guaranteed rate of return on investment in growing the grid, as I understand it. It’s not there in the business model anyway. Do you see that coming?

…

MIKE: Well, controlling this thing which is really driving a lot of the issues that we have which is peak demand growth. The issue at the moment is that we haven’t had peak demand growth in the last few years because we haven’t had hot weather. We just don’t know how many air conditioners are out there that have never been turned on – three or four per household? People have made those investments, and when the next hot weather comes they’re going to recoup their investments by running them full bore. We don’t know what the load be like when that happens.

ANDREW: Mike’s quite right. Unless there is a change in usage, there’s the risk of this ongoing growth in demand and the ongoing necessity for investment in the network, and a continued increase in prices. That is the key to it. Then the question becomes who’s responsible for managing the demand? Ought it to be the businesses themselves, and providing the businesses with the incentives to go for the lowest cost solution, whether that is network augmentation or demand management. That’s a very good way of approaching it. The other is to look at the pricing structures such that those consumers who are putting the extra load on the network, with the four air conditioners, are paying for their load on the network. At the moment everybody pays on the basis of average use rather than paying for how much demand they put on the network. Now that’s a pretty radical change in the way electricity is charged. That would lead to arguably a much better outcome in terms of the economics, it would then give people the right signals to manage their demand…

MATT: I think customers face network charges and at the moment they don’t have any way to manage their network bill because it’s just based on average usage rather than peak demand and they don’t get a signal that tells them use less peak power.

GREG: How far are we away from consumers being able to control that?

TRISTAN: In other parts of the world it’s already working. For large customers at the moment they can already do that. We have a number of customers within Victoria and Australia who when the wholesale price of power goes high they curtail their usage. Smelters who just stop hotlines for a couple of hours to reduce their usage at that point in time. The reason they can do that is they can see the price signal. They have a contract which tells them in times of high prices if you turn off you get a financial reward for doing it. And they say, it’s worth doing it, I’ll turn off. Retail customers don’t get any of those price signals at the moment.

GREG: Should they?

TRISTAN: We think they should. We think there’s about $11b of installed electricity infrastructure that’s used for about eight days a year, but no-one sees that price signal. If you’ve got something that’s not used very often, it’s very expensive. The reality is if you want people to use less of something, charge them what it costs. If they’re willing to pay it, they can use it. If they’re not willing to pay it, then they’ll do something about it. In terms of enablers, though, then you do have to have things like smart meters which allow people to actually see what’s happening in their household, and you have to have products from retailers and other participants that can allow them to do something about it. Some of the things that we’re exploring in that field are the pricing mechanisms off-time use pricing, linkages to smart appliances, so your fridge, your air conditioner, your washing machine, your dishwasher, can all be interrupted based on a price signal received by the smart meter that turns the appliance on and off. We’re getting to the point where we can do that, but we need to have the regulatory infrastructure that just enables that sort of competition and pricing to occur.

—

Demand management is an important topic for the electricity industry regardless of any questions about renewable energy.

The highlighted portion in the last statement is the key – to cope with peak demand, lots of investment has to be added that will only ever be rarely used. Earlier in the discussion (not shown in the extract) there was talk about discussing with the community the tradeoff between prices and grid reliability. Basically, making the grid 99.99% reliable imposes a lot of costs.

Maybe consumers would rather have had the option to pay 2/3 of their current bill and go without electricity for half a day every 5 years.

Imagine for example, that you live in a place with hot summers and this is scenario A:

you pay 20c per kWh

across a year you pay $2,000 for electricity

Now the next year the rules are changed and you have scenario B

you pay 12c per kWh

a saving of $800 per year on your bill

on 20 or so hot days you would pay $1 per kWh from 11am to 3pm

on one day of the year between midday and 3pm you would pay $20 per kWh.

This is all, by the way, because we can’t store electricity (not with any reasonable cost). For the same reason, before intermittent renewable energy came on the scene, economical storage of electricity was also in high demand. But it wasn’t, and still isn’t, available.

If we picture the change from scenario A to B, a lot of people would be happy. Most people would take B if it was an option. Sure it’s hot but lots of people survived without air conditioners a decade before, definitely a generation before (lots still do). Fans, ice cubes, local swimming pools, beaches.. Saving $800 a year means a lot to some people. Of course, there would be winners and losers. The losers would be the air conditioning industry which would lose a large chunk of its business; suppliers of transmission and distribution equipment no longer needed to upgrade the networks; hospitals that had to pay the high costs to keep people alive..

Of course, what actually happens in this scenario, given the regulated nature of the industry that exists in most (all?) developed countries would be a little different. As peak demand falls off, the price falls off. So it isn’t a case of no one buying electricity at $1 per kWh. What happens is the demand drops off and so the price falls. Supply and demand – an equilibrium is reached where people are willing to pay the real cost. And based on the new peak demand patterns the industry tries to forecast what it needs to upgrade or expand the network over the next 5-10 years and negotiates with the regulator how this affects prices.

But the key is people paying for the very expensive peak demand they want to use at the real cost, rather than having their costs subsidized by everyone else.

It makes perfect sense once you understand a) how an electricity grid operates and b) electricity cannot be stored.

Let’s consider a different country. Although England has some hot summers the problem of peak demand in England is a different one – cold winter evenings. Now I haven’t checked any real references but my understanding is that lots of people die indoors due to the cold each year in cold countries and it’s more of a problem than people dying due to heat in the middle of the day in hot countries. (I might be wrong on this, but I’m thinking of the subset of countries where electricity is available and affordable by the general population).

If you add demand management in a cold country maybe the problem becomes a different one – poorer old people already struggling with their electricity bills now turn off the heating when they need it the most. The cost being pushed up by prosperous working people with their heating set on the maximum for comfort. The principle is the same, of course – demand management means higher prices for electricity and so on average people use less heating.

So in my hugely over-simplified world, demand management has different questions around it in different climates. Air conditioning in the middle of the summer day as a luxury vs heating in the winter evenings as a necessity.

The problem becomes more complicated when considering renewables. Now it is less about reducing peak demand, instead about trying to match demand with a variable supply.

There are a lot of studies in demand management, essentially pilot studies, where a number of consumers get charged different rates and the study looks at the resulting reduction in electricity use. Some of them suggest possible large demand reduction, especially with intelligent meters. Some of them suggest fairly pedestrian reductions. We’ll have a look at them in the next article.

Consumer demand management can come in a few different ways:

Change in schedule – e.g., you run the dishwasher at a different time. There is no reduction in overall demand, but you’ve reduced peak demand. This is simply a choice about when to use a device, and it has little impact on you the consumer, other than minor planning, or a piece of technology that needs to be programmed

Energy storage – e.g., during winter you heat up your house during the middle of the day when demand is low – and electricity rates are low – so it’s still warm in the evening. You’ve actually increased overall demand because energy will be lost (insulation is not perfect), but you have reduced peak demand

Cutting back – e.g. you don’t turn on the airconditioning during the middle of the summer day because electricity is too expensive. In this example, you suffer some small character-building inconvenience. This is not energy use deferred or changed, it’s simply overall reduction in usage. In other examples the suffering might be substantial.

The demand management “tools” don’t create energy storage. Apart from the heat capacity of a house, reduced by less than perfect insulation, and the heat capacities of fridges and freezers, there is not much energy storage (and there’s effectively no electricity storage). So the choices come down to changing a schedule (washing machine, dishwasher) or to cutting back.

It’s easy to reduce total demand. Just increase the price.

The challenge of demand management to help with intermittent renewables also depends on whether solar or wind is the dominant energy source. We’ll look at this more in a subsequent article.

Still, for those who don’t read the paper, a few extracts from me and no surprises for readers who have worked their way through this series:

This year, we focus on Germany and its Energiewende plan (deep de-carbonization of the electricity grid in which 80% of demand is met by renewable energy), and on a California version we refer to as Caliwende.

A critical part of any analysis of high-renewable systems is the cost of backup thermal power and/or storage needed to meet demand during periods of low renewable generation. These costs are substantial; as a result, levelized costs of wind and solar are not the right tools to use in assessing the total cost of a high-renewable system

Emissions. High-renewable grids reduce CO2 emissions by 65%-70% in Germany and 55%-60% in California vs. the current grid. Reason: backup thermal capacity is idle for much of the year

Costs. High-renewable grid costs per MWh are 1.9x the current system in Germany, and 1.5x in California. Costs fall to 1.6x in Germany and 1.2x in California assuming long-run “learning curve” declines in wind, solar and storage costs, higher nuclear plant costs and higher natural gas fuel costs

Storage. The cost of time-shifting surplus renewable generation via storage has fallen, but its cost, intermittent utilization and energy loss result in higher per MWh system costs when it is added

Nuclear. Balanced systems with nuclear power have lower estimated costs and CO2 emissions than high-renewable systems. However, there’s enormous uncertainty regarding the actual cost of nuclear power in the US and Europe, rendering balanced system assessments less reliable. Nuclear power is growing in Asia where plant costs are 20%-30% lower, but political, historical, economic, regulatory and cultural issues prevent these observations from being easily applied outside of Asia

Location and comparability. Germany and California rank in the top 70th and 90th percentiles with respect to their potential wind and solar energy (see Appendix I). However, actual wind and solar energy productivity is higher in California (i.e., higher capacity factors), which is the primary reason that Energiewende is more expensive per MWh than Caliwende. Regions without high quality wind and solar irradiation may find that grids dominated by renewable energy are more costly

They also comment that they excluded transmission costs from their analysis, but this “.. could substantially increase the estimated cost of high-renewable systems..”

Their assessment of the future German system with 80% renewables:

Backup power needs unchanged. Germany’s need for thermal power (coal and natural gas) does not fall with Energiewende, since large renewable generation gaps result in the need for substantial backup capacity (see Appendix II), and also since nuclear power has been eliminated

Emissions sharply reduced. While there’s a lot of back-up thermal capacity required, for much of the year, these thermal plants are idle. Energiewende results in a 52% decline in natural gas generation vs. the current system, and a 63% decline in CO2 emissions

Cost almost double current system. The direct cost of Energiewende, using today’s costs as a reference point, is 1.9x the current system. Compared to the current system, Energiewende reduces CO2 emissions at a cost of $300 per metric ton

They contrast the renewable options (with no storage and various storage options) with nuclear:

From JP Morgan 2015

Nuclear is the bottom line in the table – the effective $ cost of CO2 reduction is vastly improved. Their comments on nuclear costs (and the uncertainties) are well worth reading.

They look at California by way of this comment:

Energiewende looks expensive, even when assuming future learning curve cost declines. Could the problem be that Germany is the wrong test case?

This is the same point I made in X – Nationalism vs Inter-Nationalism. The California example looks a lot better, in terms of the cost of reducing CO2 emissions. If your energy sources are wind and solar, and you want to reduce global CO2 emissions, it makes (economic) sense to spend your $ on the most effective method of reducing CO2.

Basically, they reach their conclusions from the following critical elements:

energy cannot be stored economically

time-series data demonstrates that, even when wind power is sourced over a very wide area, there will always be multiple days where the wind/solar energy is “a lot lower” than usual

The choices are:

spend a crazy amount on storage

build out (average) supply to many times actual demand

backup intermittent solar/wind with conventional

build a lot of nuclear power

These are obvious conclusions after reading 100 papers. The alternatives are:

ignore the time-series problem

assume demand management will save the day (more on this in a subsequent article)

In a number of earlier articles we looked at onshore wind because it is currently the lowest cost method of generating renewable electricity.

The installed onshore wind capacity (nameplate) in Europe at the start of 2015 was 121 GW. By comparison the offshore wind capacity (nameplate) by comparison was 8 GW. (Both figures from EWEA).

For recap – “nameplate” means what a wind turbine will produce at full capacity. A typical onshore wind farm in Europe will produce something like 16-30% actual output over the course of the year. If you pick some great locations in Oklahoma, you might get over 40%. It all depends on the consistency and speed of the wind. The actual output as a percentage of the nameplate capacity is usually given the term “capacity factor”. This isn’t some big disadvantage of wind – ‘it “only” produces 30% of its supposed capacity‘ – on the contrary, it’s just terminology. But it is important to check what value you are seeing in press releases and articles – so when you see that Europe has 121 GW of onshore wind installed, it usually means “nameplate”. And so the actual production of electricity, depending on location, will be something like 25-50 GW averaged over the year. End of recap..

There are three big advantages of offshore wind. And these are the reasons why a lot of money is being poured into offshore wind in Europe:

the intermittancy is lower – the wind blows more consistently

the capacity factor is higher – you get more out of your turbine, because the offshore wind speed is higher

they aren’t parked 300m from the houses of voters

In the last article XIV – Minimized Cost of 99.9% Renewable Study we saw an interesting point from one study – when storage costs were high (actually quite low, but higher than a “possible” super-low rental cost of storage from future owners of electric cars) the lowest cost method of building out the PJM network (eastern US) included a large portion of offshore wind.

This is the key to understanding the first major appeal of offshore. Intermittancy has a cost – something we will come back to again – that is a little difficult to quantify. You can smooth out the peaks and troughs by installing wind farms over a wide area, but you can’t eliminate the fact that at certain times in a given 10-year period there will be almost no wind for a week. Of course, it depends on the region, but so far even potential “super-grids” have a week’s down time (see XII – Windpower as Baseload and SuperGrids and also VIII – Transmission Costs And Outsourcing Renewable Generation)

Offshore gives you more consistent electricity production and less intermittancy.

The second point – more electricity on average from a given nameplate turbine – only helps when we consider the actual cost of different wind installations. Let’s say we put 1 GW of wind turbines onto land and these get a capacity factor of 25% – we get, on average, 250 MW. That is, across the year we get 2,190 GWh (0.25GW x 8760). Now we put 1 GW of nameplate offshore wind turbines into coastal water and we get a capacity factor of 40% on average – that is, 400 MW. So across the year we get 3,504 GWh (0.4 x 8760). This increased capacity factor only helps if the cost of installing the 1 GW of turbines offshore is less than 60% more expensive. Unfortunately, this is not the case (at the moment).

The third point is of great interest in Europe. Germany, Spain, the UK and Ireland have been installing a lot of onshore wind turbines. These are highly populated countries. For a later article, producing say 50% of each of these country’s electricity requires a lot of land area. Of course, the footprint on the actual land is quite small, but each turbine has to be some distance from every other turbine. This means that producing 15 GW of electricity from wind in the UK (about half of the average) would take up a lot of land area. The problem is more acute in Germany with a lower capacity factor.

So, those are the upsides. Now let’s look at the price tag. “If you have to ask, you can’t afford it..”

In an earlier article – IX – Onshore Wind Costs – we looked at the capex cost of onshore wind and (by the time we get into the comments) we find a current capital cost of about €1M per 1MW of (nameplate) capacity. There are lots of different numbers cited, but let’s use that for now. For people more familiar with the greenback, this is about US$1.2M per 1MW.

EWEA gives a current price tag for capex cost of offshore of €2.8 – €4.0M per 1MW of (nameplate) capacity. A larger proportion of the capital cost of offshore is the installation.

Remember that we have to factor in the “capacity factor”. So the capital cost of offshore is not 3-4x the onshore cost. If we calculate the cost based on the actual production of electricity then onshore costs (capex) something like €4M per 1MW of output and offshore costs (capex) something like €7-8M per 1MW – roughly double.

Now, we can be relatively sure of capital costs because there are enough datapoints and current installations. Governments publish figures when they are paying. Suppliers give out indicative pricing. Customers give out data on contracts.

But there are big questions about maintenance costs and, unlike onshore wind with a lot of data, this is still a little shrouded in mystery. I’ve consulted a lot of sources but it seems that, with only 9GW of offshore wind constructed in Europe – and much of this very recent – there is not enough public data to confirm any estimates.

One point only is clear (as you might expect) it is “quite a bit more” than the maintenance costs of onshore wind. The marine environment impacting on the equipment combined with the hazards of getting maintenance people out on the ocean.

So far it seems that offshore has some maintenance issues that are hard to cost up. It’s an industry still in its infancy.

Of course, to get more funding, many confident predictions are made: “Offshore wind will be cheaper than gas plants by 2020.”

Without confident predictions, maybe no one will fund the next 5 years of development. I don’t want to delve any deeper into spruiking. Let’s just accept that most of what passes for discussion in the general media, repeated on many blogs, is simply press releases from governments, lobby groups and big companies, mostly repeated without any fact checking.

It’s quite possible that offshore wind will be much lower in 2020 than it is today. There are a lot of installation issues that might be improved with the combination of volume of installations, time on the job and engineering improvements. It’s also quite possible that offshore wind won’t be a lot lower in 2020 than it is today. (See points made in Renewable Energy I).

Here is IRENA for just 2 years:

From IRENA 2012

And UKERC Offshore costs from a 2012 document:

From UKERC 2012

And another from a different UKERC document, attempting to learn from experience, with reference to wind power cost projections vs how the world actually turned out:

In the short-term costs may rise before they can fall. Cost reductions from learning can be overwhelmed in the short-term by supply chain bottlenecks, build delays and ‘teething trouble’, for example lower than expected reliability at first. There is historical precedent for technologies deployed in the power sector to demonstrate cost increases during early commercialisation before supply chains and learning from experience are firmly established

From UKERC 2013

These graphs are only presented as a reminder that predictions don’t always come true. Engineering problems are hard and optimism is easy.

I’m sure offshore wind costs will come down in the long run, but as Keynes usefully reminded us, in the long run we are all dead. So “the long run” is not so useful. Whether offshore costs will come down to onshore costs in a reasonable time frame, and whether – in this time frame – they will further come down to the cost of gas turbine electricity production is open to question. Time will tell.

I’m generally an optimist. The glass is half full. Probably it’s almost full. And lots of people don’t have much, so my glass is anyway pretty amazing. It’s only the weight of blog world articles and media (lobby groups press releases) articles on this subject that compels me to remind readers that confident predictions of the future may not be correct.

Lots of sources quote LCOE (levelized cost of electricity) – this “adds” capital cost, factored by the cost of capital (interest rates), to maintenance costs and energy costs (when we consider conventional power stations with fuel costs). As explained in previous articles, this LCOE is not so useful (i.e., it’s misleading) when we consider intermittent renewables vs dispatchable conventional electricity.

As a rule of thumb consider offshore capex wind costs to be “about double” onshore wind costs, and offshore maintenance costs to be somewhat unknown, but definitely higher than onshore costs.

These rules of thumb are as much as I have been able to establish so far.

References

Wind in Power 2014 European Statistics, published February 2015 by European Wind Energy Association (EWEA)

What would the electric system look like if based primarily on renewable energy sources whose output varies with weather and sunlight? Today’s electric system strives to meet three requirements: very high reliability, low cost, and, increasingly since the 1970s, reduced environmental impacts. Due to the design constraints of both climate mitigation and fossil fuel depletion, the possibility of an electric system based primarily on renewable energy is drawing increased attention from analysts.

Several studies (reviewed below) have shown that the solar resource, and the wind resource, are each alone sufficient to power all humankind’s energy needs. Renewable energy will not be limited by resources; on the contrary, the below-cited resource studies show that a shift to renewable power will increase the energy available to humanity.

But how reliable, and how costly, will be an electric system reliant on renewable energy? The common view is that a high fraction of renewable power generation would be costly, and would either often leave us in the dark or would require massive electrical storage.

Good question.

We do not find the answers to the questions posed above in the prior literature. Several studies have shown that global energy demand, roughly 12.5 TW increasing to 17 TW in 2030, can be met with just 2.5% of accessible wind and solar resources, using current technologies [refs below]. Specifically, Delucci and Jacobson pick one mix of eight renewable generation technologies, increased transmission, and storage in grid integrated vehicles (GIV), and show this one mix is sufficient to provide world electricity and fuels. However, these global studies do not assess the ability of variable generation to meet real hourly demand within a single transmission region, nor do they calculate the lowest cost mix of technologies.

This is also what I have found – I’ve read a number of “there’s no barrier to doing this” papers including Delucchi & Jacobson – so I was glad to find this paper. (As an aside, I question some points and assumptions in this paper, but that’s less important and brief comments on those points towards the end).

The key is investigating time series based on real demand for a region and real supply based on the actual wind and sun available.

Before we look at what they did and what they found, here are some comments that are relevant for some of our recent discussions:

In a real grid, we must satisfy varying load, and with high-penetration renewables, charging and discharging storage will at times be limited by power limits not just by stored energy. More typical studies combining wind and solar do not seek any economic analysis and/or do not look at hourly match of generation to load..

Hart and Jacobson determined the least cost mix for California of wind, solar, geothermal and hydro generation. Because their mix includes dispatchable hydro, pumped hydro, geothermal, and solar thermal with storage, their variable generation (wind and photovoltaic solar) never goes above 60% of generation. Because of these existing dispatchable resources, California poses a less challenging problem than most areas elsewhere, most or all practical renewable energy sources are variable generation, and dedicated storage must be purchased for leveling power output. We cannot draw general conclusions from the California case’s results..

The ability to reliably meet load will still be required of systems in the future, despite the variability inherent in most renewable resources. However, a review of existing literature does not find a satisfactory analysis of how to do this with variable generation, nor on a regional grid-operator scale, nor at the least cost. We need to solve for all three.

What does the paper do?

Use the demand load from PJM (East Coast grid operator) for 4 years as a basis for assessing the cost-minimized solution – with the average load being 31.5 GW

Assign a cost (unsubsidized) to each type of renewable resource: onshore wind, offshore wind, solar based on 2008 costs and forecasts for 2030 costs (roughly 50% of 2008 capex costs with similar O&M costs)

And then run through nearly 2 billion or so combinations to first ensure demand is met, then secondly calculate the cost of each combination

From Budischak et al 2013

Figure 1

An most important note for me, something we will review in future articles, rather than here, is the very low cost assigned to storage using vehicle batteries – at $32/kWh, whereas centralized storage is $318/kWh. It’s clear, as we will see, that storage costs skew the analysis strongly.

Here was their lowest cost solution for 30%, 90% and 99.9% renewables. The results are probably not so surprising to people who’ve followed the series so far. Energy Produced GWa is basically the average power over the year (so 8760 GWh, which is a constant 1GW all year = 1 GWa):

From Budischak et al 2013

Figure 2

So we can see that the lowest cost method of matching demand is to produce almost 3 times the required demand. That is, the energy produced across the year averages at 91.3 GW (and appears to have peaks around 200GW). This is because storage costs so much – and because supply is intermittent. Here is the time series – click to expand:

From Budischak et al 2013

Figure 3 – Click to Expand

We see that the energy in storage (middle row) is pulled down in summer, which the paper explains as due to less supply in summer (generally less wind).

Here is a challenging week in detail, the top graph shows the gaps that need to be filled in with storage, the bottom graphs with the gaps filled by storage and also how much supply is “spilled“:

From Budischak et al 2013

Figure 4 – Click to Expand

Here is the mix of generation and storage for each of the 30%, 90%, 99.9% each under the two cost assumptions of 2008 and 2030:

From Budischak et al 2013

Figure 5 – Click to Expand

Looking at the 99.9% cases we see that the projected solar PV cost in 2030 means it has a bigger share compared with wind but that wind is still the dominant power source by a long way. (We will investigate offshore wind costs and reliability in a future article).

Costs

The paper assesses that generating 30% of power from renewables today is already cheaper than conventional generation, and producing 90% in 2030 will be cheaper than conventional generation, with 99.9% at parity.

The key point I would like to draw readers attention to, is that unlike conventional generation, the higher the penetration of renewables the more expensive the solution (because the intermittency is then a bigger problem and so requires a more costly solution).

I’m not clear how they get to the result of renewables already being cheaper than conventional (for a 30% penetration). Their wind power cost from 2008 is roughly double what we found from a variety of sources (see IX – Onshore Wind Costs & XI – Cost of Gas Plants vs Wind Farms) and we found – depending on the gas price and the discount rate – that wind at that price was generally somewhat more expensive than gas. Using current US gas prices this is definitely the case. The authors comment that there are significant subsidies for conventional generation – I have not dug into that as yet.

The cost of storage seems low. If we take instead their cost of centralized storage – $318/kWh – and look at the lowest-cost solution to meet demand we find quite a different result. First, there is a lot less storage – 360 vs 891 GWh. That’s because it’s so pricey.

Second, although the final cost per kWh of energy is not given, we can see that whereas in the GIV storage case we build 16GW solar, 90GW offshore wind, 124GW inland wind = 230GW peak, with centralized storage we build 50, 129, 61 = 240GW peak and probably need the expensive offshore wind as a more reliable (less intermittent) source than onshore wind.

My basic calculation from his data is that the capital cost of the best case central storage solution is 45% more than the GIV storage solution. And more offshore wind will definitely require additional transmission cost (which was not included in the study).

I like their approach. What is clear is that finding the best cost solution depends heavily on the cost of storage, and the mix is radically different for different storage costs. Again, it is the intermittent nature of renewables for the region in question that shapes the result.

Questions on the Analysis

We simplify our grid model by assuming perfect transmission within PJM (sometimes called a “copper plate” assumption), and no transmission to adjacent grids. We also simplify by ignoring reserve requirements, within-hourly fluctuations and ramp rates; these would be easily covered with the amount of fast storage contemplated here. In addition, we assume no preloading of storage from fossil (based on forecasting) and no demand-side management. Adding transmission would raise the costs of the renewable systems calculated here, whereas using adjacent grids, demand management, and forecasting all would lower costs. We judge the latter factors substantially larger, and thus assert (without calculation) that the net effect of adding all these factors together would not raise the costs per kWh above those we calculate below.

Their analysis consumed a lot of computing resources. Adding transmission costs would add another level of complexity. However, I don’t agree with the conclusion that the transmission costs would be offset by adjacent grids, demand management and forecasting.

In brief:

Adjacent grids have the exact same problem – the wind and solar are moving approximately in sync – meaning supply in adjacent regions is quite highly correlated; and hot and cold temperatures are likewise in sync so air-conditioning and heating demand is similar in adjacent regions – therefore another region will be drawing on their storage at the same times as the PJM region. Also, “using adjacent grids” means adding even longer transmission lines of very high capacity. That has a cost.

“Demand management” is possibly a mythical creation to solve the problem of demand being at the “wrong time”. Apart from paying big industrials to turn off power during peak demand, which is already in play for most grid operators, it apparently equates to people not turning on the heating in the cold weather – or to people buying expensive storage. I will be looking for research with some data that puts “demand management” into some reality-based focus.

Forecasting doesn’t exactly help, unless you have demand management. Better wind forecasting currently helps grid operators because it allows them to buy reserve (conventional generation) at the right time, making a more efficient use of conventional generation. I can’t see how it helps a mostly renewable scenario to be more cost-effective. Perhaps someone can explain to me what I am missing.

And I will dig into storage costs in a future article.

Conclusion

The paper is very good overall – their approach is the important aspect. There are a great many papers which all confidently state that there is no technical barrier to 100% renewables. This is true. But maybe two or three papers is enough.

If you add “enough” wind farms and “enough” solar and “enough” storage – along with “enough” transmission – you can make the grid work. But what is the cost and how exactly are you going to solve the problems? After the first few papers to consider this question, any subsequent ones that don’t actually cover the critical problem of electricity grids with intermittent renewables are basically a waste of time.

What is the critical problem? Given that storage is extremely expensive, and given the intermittent nature of renewables with the worst week of low sun and low wind in a given region – how do you actually make it work? Because yes, there is a barrier to making a 100% renewable network operate reliably. It’s not technical, as such, not if you have infinite money..

It should be crystal clear that if you need 500GW of average supply to run the US you can’t just build 500GW of “nameplate” renewable capacity. And you can’t just build 500GW / capacity factor of renewable capacity (e.g. if we required 500GW just from wind we would build something like 1.2-1.5TW due to the 30-40% capacity factor of wind) and just add “affordable storage”.

So, there is no technical barrier to powering the entire US from a renewable grid with lots of storage. Probably $50TR will be enough for the storage. Or forget the storage and just build 10x the nameplate of wind farms and have a transmission grid of 500GW around the entire country. Probably the 5TW of wind farms will only cost $5TR and the redundant transmission grid will only cost $20TR – so that’s only $25TR.

Hopefully, the point is clear. It’s a different story from dispatchable conventional generation. Adding up the possible total energy from wind and solar is step 1 and that’s been done multiple times. The critical item, missing from many papers, is to actually analyze the demand and supply options with respect to a time series and find out what is missing. And find some sensible mix of generation and storage (and transmission, although that was not analyzed in this paper) that matches supply and demand.

So this paper has a lot of merit.

It shows with their storage costs (which seem very low), that the lowest cost solution to building a 99.9% renewable network in one (reasonable sized) region is to build nearly 3 times the actual supply needed (this is not a “capacity factor” issue – see note 2).

In future articles we will look at storage costs, as I have questions about their costing. But the main points from this paper are more than enough for one article.

Notes

Note 2: The 2-3 overbuilding is not the nameplate vs capacity factor question. Let me explain. Imagine we are only talking about wind. If we build 3GW of wind farms we might get 1GW of average output across a year. This is a 33% capacity factor. The % depends on the wind turbines and where they are located.

Now if we need to get 1GW average across the year and meet demand 99.9% of the time, the lowest cost solution won’t be to build 3GW of nameplate (=1GW of average output) and add lots of storage, instead it will be to build 9GW of nameplate and some storage.